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Polotno

How Brand Ninja boosted their AI-powered content creation with Polotno

KEY HIGHLIGHTS

6+ months saved

Polotno helped Brand Ninja trim 6+ months from their launch timeline

Cut the need for 2+ devs

Brand Ninja used Polotno to launch without adding more devs to the team.

Launched in 1 week

Brand Ninja went from initial concept to launch in a week using Polotno.

6+ months saved

Polotno helped Brand Ninja trim 6+ months from their launch timeline

Cut the need for 2+ devs

Brand Ninja used Polotno to launch without adding more devs to the team.

Launched in 1 week

Brand Ninja went from initial concept to launch in a week using Polotno.

COMPANY

Brand Ninja is an Australian AI platform building AI teammates for content creation, primarily serving sports and media organizations. The product began with text generation and expanded into automated image and video content for social media distribution.

Challenge

Brand Ninja needed a scalable way to expand beyond text and deliver rich visuals for social media. Building a custom editor for images and video would have taken months, required multiple engineers, and stalled their go-to-market. Hiring more devs wasn’t cost-effective, and relying on fragmented vendor tools risked slowing growth.

Solution

Polotno gave Brand Ninja a fast, scalable platform for image and video generation. With cloud rendering and JSON templates, they automated visual creation, launched in a week, and avoided months of dev work. The integration cut costs, reduced vendor complexity, and let their team stay focused on AI-driven content.

Challenge

Solution

THE CHALLENGE

Text alone wasn't enough

Brand Ninja initially focused on large language models for text generation. As the platform matured, they recognized that social media performance depends on visual content — images and video, not just copy.

Building an internal image and video editor would have required months of engineering time and at least two additional developers. That investment would delay go-to-market and divert resources away from core AI development.

Relying on fragmented third-party tools would introduce workflow complexity and external dependencies. They needed a programmable visual generation layer that could integrate directly into their AI pipeline.

THE SOLUTION

Embedding a template-driven rendering engine

Brand Ninja integrated Polotno to power automated image and video generation through JSON templates and cloud rendering.

The integration was completed in under one week.

Instead of building a custom canvas engine, they embedded a production-ready rendering layer directly into their system. Templates are dynamically populated through AI-generated content, rendered in the cloud, and delivered without manual design intervention.

This eliminated an estimated 6+ months of internal development and removed the need to hire additional engineers.

"Polotno was just a great way for us to get into template-driven image and now video generation without having to build our own functionality in that space".

Brad Bond

CTO / Co-founder @ Brand Ninja

THE OUTCOME

Scaling smarter with Polotno: faster dev, lower costs, greater flexibility

Accelerated product expansion

Brand Ninja moved from concept to production in under a week, compressing a multi-month development effort into a rapid integration cycle.

Preserved engineering focus

The team avoided allocating two or more engineers to build and maintain custom visual infrastructure. Resources remained concentrated on AI model development and product strategy.

Automated visual production

Through cloud rendering and structured templates, Brand Ninja now generates visual content at high daily volume without manual design workflows.

Operational simplification

By consolidating visual generation into a single infrastructure layer, they avoided fragmented vendor tooling and reduced system complexity.

Brand Ninja did more than add a design tool to their product – they embedded a rendering layer that allowed their AI platform to evolve beyond text. All without slowing their roadmap or expanding their team. For AI-first platforms expanding into visual generation, the editing layer is production infrastructure, not a feature.